Understanding the behavior of workshop facilitators in systems analysis and design projects: Developing theory from process modeling projects

Organizational and technological systems analysis and design practices such as process modeling have received much attention in recent years. However, while knowledge about related artifacts such as models, tools, or grammars has substantially matured, little is known about the actual tasks and interaction activities that are conducted as part of analysis and design acts. In particular, key role of the facilitator has not been researched extensively to date. In this paper, we propose a new conceptual framework that can be used to examine facilitation behaviors in process modeling projects. The framework distinguishes four behavioral styles in facilitation (the driving engineer, the driving artist, the catalyzing engineer, and the catalyzing artist) that a facilitator can adopt. To distinguish between the four styles, we provide a set of ten behavioral anchors that underpin facilitation behaviors. We also report on a preliminary empirical exploration of our framework through interviews with experienced analysts in six modeling cases. Our research provides a conceptual foundation for an emerging theory for describing and explaining different behaviors associated with process modeling facilitation, provides first preliminary empirical results about facilitation in modeling projects, and provides a fertile basis for examining facilitation in other conceptual modeling activities.

Fast and accurate business process drift detection

Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs – also known as business process drift detection – enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent it is applicable for online drift detection.

Log delta analysis: Interpretable differencing of Business process event logs

This paper addresses the problem of identifying and explaining behavioral differences between two business process event logs. The paper presents a method that, given two event logs, returns a set of statements in natural language capturing behavior that is present or frequent in one log, while absent or infrequent in the other. This log delta analysis method allows users to diagnose differences between normal and deviant executions of a process or between two versions or variants of a process. The method relies on a novel approach to losslessly encode an event log as an event structure, combined with a frequency-enhanced technique for differencing pairs of event structures. A validation of the proposed method shows that it accurately diagnoses typical change patterns and can explain differences between normal and deviant cases in a real-life log, more compactly and precisely than previously proposed methods.

Evidence-based business process management: Using digital opportunities to drive organizational innovation

Process improvement and innovation are risky endeavors, like swimming in unknown waters. In this chapter, I will discuss how process innovation through BPM can benefit from Research-as-a-Service, that is, from the application of research concepts in the processes of BPM projects. A further subject will be how innovations can be converted from confidence-based to evidence-based models due to affordances of digital infrastructures such as large-scale enterprise soft-ware or social media. I will introduce the relevant concepts, provide illustrations for digital capabilities that allow for innovation, and share a number of key takeaway lessons for how organizations can innovate on the basis of digital opportunities and principles of evidence-based BPM: the foundation of all process decisions in facts rather than fiction.

Process visualization techniques for multi-perspective process comparisons

Organizations executing similar business processes need to understand the differences and similarities in activities performed across work environments. Presently, research interest is directed towards the potential of visualization for the display of process models, to support users in their analysis tasks. Although recent literature in process mining and comparison provide several methods and algorithms to perform process and log comparison, few contributions explore novel visualization approaches. This paper analyses process comparison from a design perspective, providing some practical visualization techniques as anal- ysis solutions (/to support process analysis). The design of the visual comparison has been tackled through three different points of view: the general model, the projected model and the side-by-side comparison in order to support the needs of business analysts. A case study is presented showing the application of process mining and visualization techniques to patient treatment across two Australian hospitals.

Liquid Business Process Model Collections

Many organizations realize that increasing amounts of data (“Big Data”) need to be dealt with intelligently in order to compete with other organizations in terms of efficiency, speed and services. The goal is not to collect as much data as possible, but to turn event data into valuable insights that can be used to improve business processes. However, data-oriented analysis approaches fail to relate event data to process models. At the same time, large organizations are generating piles of process models that are disconnected from the real processes and information systems. In this chapter we propose to manage large collections of process models and event data in an integrated manner. Observed and modeled behavior need to be continuously compared and aligned. This results in a “liquid” business process model collection, i.e. a collection of process models that is in sync with the actual organizational behavior. The collection should self-adapt to evolving organizational behavior and incorporate relevant execution data (e.g. process performance and resource utilization) extracted from the logs, thereby allowing insightful reports to be produced from factual organizational data.

BPMN Miner: Automated Discovery of BPMN Process Models with Hierarchical Structure

Existing techniques for automated discovery of process models from event logs gen- erally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of hierarchical BPMN models con- taining interrupting and non-interrupting boundary events and activity markers. The technique employs functional and inclusion dependency discovery techniques in order to elicit a process-subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are then discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery tech- niques, it is possible to filter out noise in the event log arising for example from data entry errors or missing events. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

The transition to palliative care: A critical exploration in the context of metastatic melanoma

This research explored the transition to palliative care process through critical analysis of the experiences of patients diagnosed with metastatic melanoma, family carers and health professionals. The outcomes depict a complex intersection between acute care services and palliative care where the discipline of palliative care struggled to position itself within a highly specialised health system. The findings indicate uncertainty around scopes of practice with ambiguity and tension around the transition to palliative care. The research thus argues for stronger and more coherent partnerships and a critical and interdisciplinary conversation about the positioning of palliative care in the acute care sector.

Investigation of the effects of the fatty acid profile on fuel properties using a Multi-Criteria Decision Analysis

The structural features of fatty acids in biodiesel, including degree of unsaturation, percentage of saturated fatty acids and average chain length, influence important fuel properties such as cetane number, iodine value, density, kinematic viscosity, higher heating value and oxidation stability. The composition of fatty acid esters within the fuel should therefore be in the correct ratio to ensure fuel properties are within international biodiesel standards such as ASTM 6751 or EN 14214. This study scrutinises the influence of fatty acid composition and individual fatty acids on fuel properties. Fuel properties were estimated based on published equations, and measured according to standard procedure ASTM D6751 and EN 14214 to confirm the influences of the fatty acid profile. Based on fatty acid profile-derived calculations, the cetane number of the microalgal biodiesel was estimated to be 11.6, but measured 46.5, which emphasises the uncertainty of the method used for cetane number calculation. Multi-criteria decision analysis (MCDA), PROMETHEE-GAIA, was used to determine the influence of individual fatty acids on fuel properties in the GAIA plane. Polyunsaturated fatty acids increased the iodine value and had a negative influence on cetane number. Kinematic viscosity was negatively influenced by some long chain polyunsaturated fatty acids such as C20:5 and C22:6 and some of the more common saturated fatty acids C14:0 and C18:0. The positive impact of average chain length on higher heating value was also confirmed in the GAIA plane

Heathcote’s practice-led teaching: Pioneering research as well as pioneering pedagogy

It is widely recognized that Dorothy Heathcote was a dynamic and radical teacher who transformed and continually reinvented drama teaching. She did this by allowing her emerging thinking and understandings to flow from, and be tested by, regular and intensive ‘practicing’ in the classroom. In this way theoretical claims were grounded and evidenced in authentic classroom practice. And yet, for all her impact, it is rare to hear the claim that Heathcote’s pedagogic breakthroughs resulted from a legitimate research methodology. Clever and charismatic teaching yes; research no. One of the world’s best teachers certainly, but not a researcher; even though every lesson was experimental and every classroom was a site for discovery. This paper investigates that conundrum firstly by acknowledging that Heathcote’s practice-led teaching approach to discovery did not map comfortably on to the established educational research traditions of the day. It argues that traditional research methodologies, with their well-established protocols and methods, could not understand or embrace a research process which does its work by creating ‘fictional realities’ of openness, allegory and uncertainty. In recent years however it can be seen that Heathcote’s practice led-teaching, so essential for advancing the field, closely aligns with what many contemporary researchers are now calling practice-led research or practice as research or, in many Nordic countries, artistic research. A form of performative research, practice-led research has not emerged from the field of education but rather from the creative arts. Seeking to develop ways of researching creative practice which is deeply sympathetic and respectful of that practice, artist-researchers have developed practice-led research “which is initiated in practice, where questions, problems, challenges are identified and formed by the needs of practice and practitioners” (Grey, 1996). This sits comfortably with Heathcote’s classroom priority of “discovering by trial, error and testing; using available materials with respect for their nature, and being guided by this appreciation of their potential” (Heathcote, 1967). The paper will conclude by testing the dynamics of Heathcote’s practice-led teaching against the six conditions of practice-led research (Haseman&Mafe, 2011), a testing which will allow for a re-interpretation and re-housing of Dorothy Heathcote’s classroom-based teaching methodology as a form of performative research in its own right.

Nursing and medical perceptions of a hospital rapid response system: New process but same old game?

Perhaps no other patient safety intervention depends so acutely on effective interprofessional teamwork for patient survival than the hospital rapid response system (RRS). Yet little is known about nurse-physician relationships when rescuing at-risk patients. This study compared nursing and medical staff perceptions of a mature RRS at a large tertiary hospital. Findings indicate the RRS may be failing to address a hierarchical culture and systems-level barriers to early recognition and response to patient deterioration.

Process quality indicators targeting cognitive impairment to support quality of care for older people with cognitive impairment in emergency departments

Objectives The objective of this study was to develop process quality indicators (PQIs) to support the improvement of care services for older people with cognitive impairment in emergency departments (ED). Methods A structured research approach was taken for the development of PQIs for the care of older people with cognitive impairment in EDs, including combining available evidence with expert opinion (phase 1), a field study (phase 2), and formal voting (phase 3). A systematic review of the literature identified ED processes targeting the specific care needs of older people with cognitive impairment. Existing relevant PQIs were also included. By integrating the scientific evidence and clinical expertise, new PQIs were drafted and, along with the existing PQIs, extensively discussed by an advisory panel. These indicators were field tested in eight hospitals using a cohort of older persons aged 70 years and older. After analysis of the field study data (indicator prevalence, variability across sites), in a second meeting, the advisory panel further defined the PQIs. The advisory panel formally voted for selection of those PQIs that were most appropriate for care evaluation. Results In addition to seven previously published PQIs relevant to the care of older persons, 15 new indicators were created. These 22 PQIs were then field tested. PQIs designed specifically for the older ED population with cognitive impairment were only scored for patients with identified cognitive impairment. Following formal voting, a total of 11 PQIs were included in the set. These PQIs targeted cognitive screening, delirium screening, delirium risk assessment, evaluation of acute change in mental status, delirium etiology, proxy notification, collateral history, involvement of a nominated support person, pain assessment, postdischarge follow-up, and ED length of stay. Conclusions This article presents a set of PQIs for the evaluation of the care for older people with cognitive impairment in EDs. The variation in indicator triggering across different ED sites suggests that there are opportunities for quality improvement in care for this vulnerable group. Applied PQIs will identify an emergency services' implementation of care strategies for cognitively impaired older ED patients. Awareness of the PQI triggers at an ED level enables implementation of targeted interventions to improve any suboptimal processes of care. Further validation and utility of the indicators in a wider population is now indicated.

Effect and process evaluations of an early childhood obesity prevention intervention

The thesis develops a summative evaluation framework of the NOURISH Randomised Controlled Trial, and applies this to a selection of maternal feeding choice outcomes of the programme. The research is based on an ecological model of the complex set of factors that need to be acted upon to address childhood obesity. The novel approach extends the standard RCT effect evaluation, while also conducting an in-depth evaluation of the process of programme development and implementation. Research findings identify adaptations of intervention delivery that could improve its effectiveness and translatability, in a new cycle of the programme.

Analysis and improvement of a construction permit approval process: A teaching case for developing business process development capabilities, targeting developing nations

With the increasing competitiveness in global markets, many developing nations are striving to constantly improve their services in search for the next competitive edge. As a result, the demand and need for Business Process Management (BPM) in these regions is seeing a rapid rise. Yet there exists a lack of professional expertise and knowledge to cater to that need. Therefore, the development of well-structured BPM training/ education programs has become an urgent requirement for these industries. Furthermore, the lack of textbooks or other self-educating material, that go beyond the basics of BPM, further ratifies the need for case based teaching and related cases that enable the next generation of professionals in these countries. Teaching cases create an authentic learning environment where complexities and challenges of the ‘real world’ can be presented in a narrative, enabling students to evolve crucial skills such as problem analysis, problem solving, creativity within constraints as well as the application of appropriate tools (BPMN) and techniques (including best practices and benchmarking) within richer and real scenarios. The aim of this paper is to provide a comprehensive teaching case demonstrating the means to tackle any developing nation’s legacy government process undermined by inefficiency and ineffectiveness. The paper also includes thorough teaching notes The article is presented in three main parts: (i) Introduction - that provides a brief background setting the context of this paper, (ii) The Teaching Case, and (iii) Teaching notes.